𝗪𝗵𝘆 𝗜 𝗦𝘁𝗼𝗽𝗽𝗲𝗱 𝗥𝗲𝗹𝘆𝗶𝗻𝗴 𝗼𝗻 𝗮 𝗦𝗶𝗻𝗴𝗹𝗲 𝗔𝗜 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗿
I built a real-time chatbot for a community forum. I thought using one API would be enough. I was wrong.
Three weeks in, I hit a 5xx error during peak hours. My chatbot went dark. Users were frustrated. I realized I cannot trust one provider for production apps.
I used GPT-4. It worked well until it did not. I faced rate limits, timeouts, and complete outages. Paying for higher tiers felt like fixing a symptom instead of the problem.
I tried other providers, but they all had different formats and auth methods. My code became a mess of switch-case statements. I needed a system to:
- Hide provider differences.
- Switch to a backup provider on failure.
- Cache responses.
- Avoid vendor lock-in.
I avoided third-party libraries because they were too complex and broke easily. Instead, I built a simple router.
First, I defined a common interface for all providers. Each provider implements a generate method and a health check.
Next, I built a router class. It tries providers in a specific order. It uses exponential backoff and a simple cache. If the first provider fails, the system waits and tries the next one.
This system saved my weekends during three different outages. It keeps my app running even when a major provider goes down.
If you build this, keep these points in mind:
- Use Redis for caching in production instead of a local dictionary.
- Health checks do not guarantee success. A provider might pass a check but fail a real request.
- Parse Retry-After headers to handle rate limits properly.
- Add logging for token usage to track costs.
- Use asyncio for higher performance.
If your project is small, do not over-engineer. If you need streaming, this pattern adds latency. Choose the right tool for your scale.
How do you handle provider reliability? Do you stick to one provider or build a fallback layer?
Optional learning community: https://t.me/GyaanSetuAi